What are the most pressing fundamental questions and gaps in AI research?
According to IBM Research organization in the response to the White House as part of preparing for the future of Artificial Intelligence, AI depends upon many long-term advances, not only from AI researchers but from many interdisciplinary teams of experts from many disciplines, including the following challenges:

Machine learning and reasoning.
Currently, AI systems use supervised learning using a huge amount of dataset of labelled data for training. This is very different to how humans learn by creating concepts, relationship, common sense reasoning which gives the ability to learn much without too much data. Therefore machine learning with common-sense reasoning capabilities should be researched further more.

Decision techniques.
Current AI-based systems have very limited ability for making decisions, therefore new techniques must be developed (e.g. modeling systemic risks, analyzing tradeoffs, detecting anomalies in context, analyzing data while preserving privacy).

Domain-specific AI systems.
The current AI-based system is lack of abilities to understand the variety of domains of human expertise (such as medicine, engineering, law and many more). The systems should be able to perform professional-level tasks such as designing problems, experiments, managing contradictions, negotiating, etc.

Data assurance and trust.
The current AI-based systems require huge amounts of data and their behaviour directly depends on the quality of this data which can be biased, incomplete or compromised. This can be expensive and time-consuming especially where it is used for safety-critical systems which potentially can be very dangerous.

Radically efficient computing infrastructure.
The current AI-based systems require unprecedented workloads and computing power which require the development of new computing architectures (such as neuromorphic).

Interpretability and explanations.
For people to follow AI suggestions, they need to trust systems, and this is only when they are capable of knowing users' intents, priorities, reasoning and they can learn from their mistakes. These capabilities are required in many business domains and professionals

Value alignment and ethics.
Humans can share the common knowledge of how the world function, the machine cannot. They can fail by having unintended and unexpected behaviour only because humans did not specify the right goals for them or they omitted essential training details. The systems should be able to correct the specification of the goals and avoid unintended and undesired consequences in the behaviour.

Social AI.
The AI-based systems should be able to work closely with humans in their professional and personal life, therefore they should have significant social capabilities because they can impact our emotions and our decision-making capabilities. Also, sophisticated natural language capabilities will need to be developed to allow a natural interaction and dialogue between humans and machines.


Source: Fundamental questions in AI research, and the most important research gaps (RFI questions 5 and 6). (link no longer available)